system

Abstract

The system according to this embodiment aims to streamline the entire process from meeting preparation and operation to the creation of meeting minutes. [Solution] The system according to the embodiment comprises a reception unit, an agenda creation unit, a schedule confirmation unit, a speech facilitation unit, a meeting minutes creation unit, and an email distribution unit. The reception unit inputs the theme and purpose of the meeting. The agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit. The schedule confirmation unit checks the participants' schedules and presents possible dates. The speech facilitation unit encourages participation during the meeting and provides summaries of the content and closing comments. The meeting minutes creation unit automatically creates meeting minutes after the meeting. The email distribution unit distributes the meeting minutes via email.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of labor and time are required for meeting preparation, operation, and minutes creation.

[0005] The system according to the embodiment aims to streamline the process from meeting preparation to operation and minutes creation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an agenda creation unit, a schedule confirmation unit, a speech facilitation unit, a meeting minutes creation unit, and an email distribution unit. The reception unit inputs the theme and purpose of the meeting. The agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit. The schedule confirmation unit checks the participants' schedules and presents possible dates. The speech facilitation unit encourages participation during the meeting and provides summaries of the content and closing comments. The meeting minutes creation unit automatically creates meeting minutes after the meeting. The email distribution unit distributes the meeting minutes via email. [Effects of the Invention]

[0007] The system according to this embodiment can streamline everything from meeting preparation and operation to the creation of meeting minutes. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The meeting preparation efficiency system according to an embodiment of the present invention is a system that utilizes generative AI to streamline meeting preparation. This meeting preparation efficiency system automatically creates a draft agenda before the meeting, checks participants' schedules, and presents possible dates. During the meeting, the generative AI encourages participation and provides summaries of the content and closing comments. After the meeting, the generative AI automatically creates meeting minutes and distributes them via email. This mechanism supports and streamlines the entire meeting operation. For example, the generative AI automatically creates a draft agenda before the meeting. When a user inputs the theme and purpose of the meeting, the generative AI generates a draft agenda based on that. For example, if the theme of the meeting is "new product development," the generative AI automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." This significantly reduces the time required for meeting preparation. Next, the generative AI checks participants' schedules and presents possible dates. The generative AI proposes the optimal meeting date based on the participants' calendar information. For example, by automatically providing candidate dates that take everyone's availability into consideration, scheduling can be adjusted smoothly. This allows for the rapid scheduling of meetings. During the meeting, the generating AI encourages participation and provides summaries and closing comments. The generating AI provides comments in real time to encourage participation and support the progress of the discussion. For example, if the discussion stalls, the generating AI will provide a comment such as, "Let's move on to the next agenda item." It also supports the efficient conclusion of the meeting on time by presenting summaries of the content and providing closing comments in accordance with the progress of the meeting. After the meeting, the generating AI automatically creates meeting minutes and distributes them via email. The generating AI automatically summarizes the content of the discussion during the meeting and creates the minutes. For example, it extracts the important points and decisions of the meeting and compiles them into minutes. Furthermore, the generating AI automatically creates emails and distributes the minutes to participants. This allows for rapid information sharing and smooth follow-up after the meeting. In this way, by utilizing the generating AI, the entire process from meeting preparation to progress and follow-up can be streamlined and handled without any omissions or errors.This provides support for all aspects of meeting management and improves operational efficiency. The meeting preparation efficiency system streamlines the entire process, from preparation to execution and follow-up, ensuring no omissions or errors are made.

[0029] The meeting preparation efficiency system according to this embodiment comprises a reception unit, an agenda creation unit, a schedule confirmation unit, a discussion facilitation unit, a meeting minutes creation unit, and an email distribution unit. The reception unit inputs the theme and purpose of the meeting. The reception unit provides, for example, an interface for the user to input the theme and purpose of the meeting. For example, the reception unit allows the user to input the theme and purpose of the meeting in text format. The reception unit also allows the user to input the theme and purpose of the meeting using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text and inputs it as the theme and purpose of the meeting. The agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit. The agenda creation unit generates a draft agenda based on the theme and purpose of the meeting, for example, using generation AI. For example, if the theme of the meeting is "new product development," the agenda creation unit automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." The schedule confirmation unit checks the participants' schedules and presents possible dates. The Schedule Confirmation Unit, for example, suggests the optimal meeting date based on participants' calendar information. For example, the Schedule Confirmation Unit automatically generates candidate dates that take everyone's availability into consideration, facilitating smooth scheduling. The Speech Facilitation Unit encourages participation during the meeting and provides summaries of the content and closing comments. For example, the Speech Facilitation Unit uses generational AI to provide comments that encourage participation in real time and comments that support the progress of the discussion. For example, if the discussion stalls, the Speech Facilitation Unit provides comments such as, "Let's move on to the next agenda item." The Speech Facilitation Unit also presents summaries of the content and provides closing comments in accordance with the progress of the meeting. The Minutes Creation Unit automatically creates meeting minutes after the meeting. For example, the Minutes Creation Unit uses generational AI to automatically summarize the content of the discussion during the meeting and create the minutes. For example, the Minutes Creation Unit extracts important points and decisions from the meeting and compiles them into minutes. The Email Distribution Unit distributes the meeting minutes via email. The email distribution department, for example, uses generation AI to automatically create emails based on the generated meeting minutes and distributes them to participants.For example, the email distribution department automatically compiles meeting minutes, including key points and decisions, into emails and distributes them to participants. This allows the meeting preparation efficiency system, according to this embodiment, to streamline the entire process from meeting preparation to execution and follow-up, ensuring no information is missed or overlooked.

[0030] The reception desk inputs the meeting theme and purpose. For example, the reception desk provides an interface for users to input the meeting theme and purpose. Specifically, it features an intuitive graphical user interface (GUI) that allows users to easily input information using text boxes and dropdown menus. The reception desk also allows users to input the meeting theme and purpose using voice input. Using speech recognition technology, it converts the user's voice into text and inputs it as the meeting theme or purpose. For example, if a user voice-inputs, "The theme of the next meeting is new product development," the system converts this into text and registers it as the meeting theme. Furthermore, the reception desk also has a function to automatically suggest similar themes and purposes by referring to past meeting data. This allows users to efficiently set themes and purposes while referring to past meeting content. For example, if a meeting on "new product development" was held in the past, the system will refer to the minutes and agenda and automatically suggest relevant information. In this way, the reception desk supports users in quickly and accurately inputting meeting themes and purposes.

[0031] The agenda creation department automatically generates a draft agenda based on information entered by the reception department. For example, using a generative AI, the agenda creation department generates a draft agenda based on the meeting's theme and objectives. Specifically, the generative AI learns from past meeting data and relevant literature to suggest optimal agenda items. For example, if the meeting theme is "New Product Development," it automatically creates agenda items such as "Market Research Results Report," "Development Schedule Confirmation," and "Budget Review." The generative AI uses natural language processing technology to analyze the theme and objectives entered by the user and extract relevant keywords and topics. This allows the agenda creation department to generate a draft agenda that accurately reflects the user's intentions. Furthermore, the agenda creation department presents the generated draft agenda to the user and provides an interface that allows for modifications and additions as needed. For example, if a user wants to delete the "Market Research Results Report" item, they can easily do so. The agenda creation department also has a function to update the agenda in real time according to the progress of the meeting. This allows the agenda creation section to support users in efficiently creating meeting agendas and adapting to changes flexibly.

[0032] The schedule confirmation unit checks participants' schedules and presents potential dates. For example, it suggests the optimal meeting date based on participants' calendar information. Specifically, the schedule confirmation unit collects each participant's calendar information and analyzes their availability. This allows for smooth scheduling by automatically generating candidate dates that consider everyone's availability. For example, the schedule confirmation unit extracts available time slots from each participant's calendar and presents non-overlapping time slots as candidate dates. The schedule confirmation unit also has a function to suggest the optimal date considering the priority and importance of each participant. For example, it prioritizes the schedules of important participants and adjusts the schedules of other participants to match their availability. Furthermore, the schedule confirmation unit can learn the frequency of meetings and past scheduling patterns to suggest the optimal meeting time. As a result, the schedule confirmation unit can efficiently and effectively schedule meetings and suggest dates that are easy for all participants to attend.

[0033] The speech facilitation unit encourages participation during meetings and provides summaries and closing comments. For example, it uses a generative AI to provide real-time comments encouraging participation and supporting the flow of discussion. Specifically, the generative AI analyzes the content of discussions and generates comments encouraging participation at appropriate times. For instance, if the discussion stalls, it might provide a comment such as, "Let's move on to the next topic." The speech facilitation unit also presents summaries of the content and provides closing comments in line with the progress of the meeting. The generative AI analyzes the content of discussions in real time, extracting key points and conclusions. This allows the speech facilitation unit to facilitate smooth meeting progress and support participants in efficiently advancing the discussion. Furthermore, the speech facilitation unit monitors the frequency and content of participants' contributions and provides an environment that encourages specific participants to speak. For example, if a particular participant is not speaking, it provides a comment encouraging them to participate. This allows the speech facilitation unit to create a meeting environment where everyone can actively participate, improving the quality of the discussion.

[0034] The meeting minutes creation unit automatically generates meeting minutes after the meeting. For example, it uses a generation AI to automatically summarize the content of the meeting and create the minutes. Specifically, the generation AI converts the audio data from the meeting into text and extracts important points and decisions. This allows the meeting minutes creation unit to accurately record the important points and decisions of the meeting and compile them into minutes. Furthermore, the meeting minutes creation unit presents the generated minutes to the user and provides an interface that allows for modifications and additions as needed. For example, if a user wants to add a specific statement, they can easily do so. The meeting minutes creation unit also has a function to update the minutes in real time according to the progress of the meeting. This allows the meeting minutes creation unit to support users in efficiently creating minutes and responding flexibly. Additionally, the meeting minutes creation unit has a function to refer to past meeting minutes data and automatically suggest similar meeting content. This allows users to efficiently create minutes while referring to past meeting content.

[0035] The email distribution department distributes meeting minutes via email. For example, it uses a generation AI to automatically create emails based on the generated meeting minutes and distribute them to participants. Specifically, the generation AI extracts key points and decisions from the minutes and generates a concise email body. This allows the email distribution department to automatically summarize meeting minutes, including key points and decisions, into emails and distribute them to participants. Furthermore, the email distribution department monitors email delivery status and sends reminders to participants who haven't read the emails. For example, if an email is not opened within a certain period, it automatically sends a reminder to ensure important information is conveyed. The email distribution department also provides an interface to manage recipient lists, allowing for additions and deletions as needed. This enables the email distribution department to efficiently and reliably distribute meeting minutes, supporting all participants in sharing important information. Additionally, the email distribution department archives the content of distributed emails for later reference. This allows for easy searching of past meeting content and quick retrieval of necessary information.

[0036] The agenda creation unit can generate a draft agenda based on the meeting's theme and objectives. For example, the agenda creation unit uses generative AI to generate a draft agenda based on the meeting's theme and objectives. For example, if the meeting's theme is "new product development," the agenda creation unit automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." This streamlines meeting preparation by generating a draft agenda based on the meeting's theme and objectives. The draft agenda may include, but is not limited to, a list of topics, time allocation, and assigned personnel. Some or all of the above-described processes in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can generate a draft agenda using a generative AI model that takes the meeting's theme and objectives as input and outputs a draft agenda.

[0037] The schedule confirmation unit can suggest the optimal meeting date based on the participants' calendar information. For example, the schedule confirmation unit can suggest the optimal meeting date based on the participants' calendar information. For example, the schedule confirmation unit can automatically generate candidate dates that take everyone's availability into consideration, thereby facilitating smooth schedule adjustments. This streamlines schedule adjustments by suggesting the optimal meeting date based on the participants' calendar information. The optimal meeting date may include, but is not limited to, everyone's free time and high-priority dates. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can suggest a meeting date using an AI model that takes participants' calendar information as input and outputs the optimal meeting date.

[0038] The speech facilitation unit can provide comments that encourage participation in real time. For example, the speech facilitation unit can provide comments that encourage participation in real time using generative AI. For example, if the discussion stalls, the speech facilitation unit can provide a comment such as, "Let's move on to the next topic." By providing comments that encourage participation in real time, the meeting proceeds more smoothly. Comments that encourage participation include, but are not limited to, asking questions or soliciting opinions. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments that encourage participation using an AI model that takes the progress of the meeting as input and outputs comments that encourage participation.

[0039] The speech facilitation unit can present a summary of the content and provide closing comments in accordance with the progress of the meeting. The speech facilitation unit can, for example, use generative AI to present a summary of the content and provide closing comments in accordance with the progress of the meeting. For example, the speech facilitation unit can present a summary of the content and provide closing comments in accordance with the progress of the meeting. This allows the meeting to conclude efficiently by presenting a summary of the content and providing closing comments in accordance with the progress of the meeting. The content summary may include, for example, how to extract key points and the length of the summary. The closing comments may include, for example, a summary of the meeting and action items for the next meeting. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide a summary of the content and closing comments using an AI model that takes the progress of the meeting as input and outputs a summary of the content and closing comments.

[0040] The minutes creation unit can automatically summarize the content of discussions during a meeting and create meeting minutes. For example, the minutes creation unit can use a generative AI to automatically summarize the content of discussions during a meeting and create meeting minutes. For example, the minutes creation unit can extract important points and decisions made during a meeting and summarize them as minutes. This streamlines the minutes creation process by automatically summarizing the content of discussions during a meeting. The content of discussions during a meeting includes, but is not limited to, the main points of the discussion and the names of the speakers. Some or all of the above-described processes in the minutes creation unit may be performed using a generative AI, or they may not. For example, the minutes creation unit can create meeting minutes using a generative AI model that takes the content of discussions during a meeting as input and outputs meeting minutes.

[0041] The email distribution unit can automatically create and distribute emails to participants based on the generated meeting minutes. For example, the email distribution unit can use generation AI to automatically create and distribute emails to participants based on the generated meeting minutes. For example, the email distribution unit can automatically summarize the meeting minutes, including important points and decisions, into an email and distribute it to participants. This allows for rapid information sharing by automatically creating and distributing emails based on the generated meeting minutes. Automatic email creation includes, but is not limited to, email templates and delivery timing. Some or all of the above processes in the email distribution unit may be performed using AI or not. For example, the email distribution unit can create emails using an AI model that takes the generated meeting minutes as input and outputs emails.

[0042] The reception desk can refer to past meeting themes and objectives and automatically suggest similar themes. For example, the reception desk can use AI to refer to past meeting themes and objectives and automatically suggest similar themes. For example, the reception desk can automatically display similar themes as candidates based on meeting themes previously entered by the user. This allows users to efficiently set meeting themes and objectives by referring to past meeting themes and objectives. Past meeting themes and objectives include, but are not limited to, database search methods and similarity calculation methods. Similar themes include, but are not limited to, keyword matching and theme relevance. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest similar themes using an AI model that takes past meeting themes and objectives as input and outputs similar themes.

[0043] The reception desk can automatically collect and present relevant materials and data to the user when the meeting theme and purpose are entered. For example, the reception desk can use AI to automatically collect and present relevant materials and data to the user when the meeting theme and purpose are entered. For example, when the user enters the meeting theme, the reception desk automatically collects and displays relevant past meeting materials. This streamlines meeting preparation by automatically collecting and presenting relevant materials and data to the user. Relevant materials and data include, but are not limited to, database search methods and material selection criteria. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can collect materials and data using an AI model that takes the meeting theme and purpose as input and outputs relevant materials and data.

[0044] The reception desk can analyze the user's past speaking history and automatically complete relevant keywords when the user inputs the meeting theme and purpose. For example, the reception desk can use AI to analyze the user's past speaking history and automatically complete relevant keywords when the user inputs the meeting theme and purpose. For example, the reception desk can automatically complete relevant themes based on keywords the user has previously spoken. This streamlines the input process by analyzing the user's past speaking history and automatically completing relevant keywords. Past speaking history includes, but is not limited to, database search methods and methods for analyzing spoken content. Relevant keywords include, but are not limited to, keyword matching rates and relevance evaluation methods. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can automatically complete keywords using an AI model that takes the user's past speaking history as input and outputs relevant keywords.

[0045] The reception desk can provide an optimal input format based on the user's job duties and position when inputting the meeting theme and purpose. For example, the reception desk can use AI to provide an optimal input format based on the user's job duties and position when inputting the meeting theme and purpose. For example, the reception desk can automatically suggest relevant themes and purposes based on the user's job duties. This streamlines input by providing an optimal input format based on the user's job duties and position. Job duties and position include, but are not limited to, job descriptions and hierarchical positions. An optimal input format includes, but are not limited to, the selection of input items and the provision of input guides. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can provide an input format using an AI model that takes the user's job duties and position as input and outputs an optimal input format.

[0046] The agenda creation unit can automatically suggest optimal items when creating an agenda by referring to past meeting agendas. For example, the agenda creation unit can use generative AI to automatically suggest optimal items when creating an agenda by referring to past meeting agendas. For example, the agenda creation unit can automatically suggest relevant items based on past meeting agendas. This streamlines agenda creation by automatically suggesting optimal items by referring to past meeting agendas. Past meeting agendas include, but are not limited to, database search methods and agenda similarity evaluation methods. Optimal items include, but are not limited to, agenda importance and relevance evaluation methods. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can automatically suggest items using a generative AI model that takes past meeting agendas as input and outputs optimal items.

[0047] The agenda creation unit can apply different agenda generation algorithms depending on the purpose of the meeting when creating an agenda. For example, the agenda creation unit can use a generative AI to apply different agenda generation algorithms depending on the purpose of the meeting when creating an agenda. For example, if the purpose of the meeting is information sharing, the agenda creation unit will generate an agenda that organizes the information. In this way, by applying different agenda generation algorithms depending on the purpose of the meeting, an agenda that suits the purpose will be generated. Agenda generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the agenda creation unit may be performed using a generative AI or not. For example, the agenda creation unit can generate an agenda using a generative AI model that takes the purpose of the meeting as input and outputs an agenda.

[0048] The agenda creation unit can customize agenda items based on the expertise of meeting participants when creating an agenda. For example, the agenda creation unit can use generative AI to customize agenda items based on the expertise of meeting participants when creating an agenda. For example, the agenda creation unit can automatically suggest relevant agenda items based on the participants' expertise. This ensures that an optimal agenda is provided for participants by customizing agenda items based on their expertise. The participants' expertise includes, but is not limited to, job descriptions and past statements. Customizing agenda items includes, but is not limited to, adding topics related to expertise or deleting unnecessary topics. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can customize agenda items using a generative AI model that takes participants' expertise as input and outputs agenda items.

[0049] The agenda creation unit can automatically incorporate the latest industry trends related to the meeting topic when creating the agenda. For example, the agenda creation unit can use generative AI to automatically incorporate the latest industry trends related to the meeting topic when creating the agenda. For example, the agenda creation unit can automatically incorporate the latest industry news related to the meeting topic. This ensures that the agenda is based on the latest information by automatically incorporating the latest industry trends related to the meeting topic. Latest industry trends include, but are not limited to, analysis of news articles and reference to industry reports. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can incorporate industry trends using a generative AI model that takes the meeting topic as input and outputs the latest industry trends.

[0050] The schedule confirmation unit can suggest the optimal meeting location when confirming the schedule, taking into account the geographical location information of the participants. For example, the schedule confirmation unit can use AI to suggest the optimal meeting location when confirming the schedule, taking into account the geographical location information of the participants. For example, the schedule confirmation unit suggests the optimal meeting location based on the geographical location information of the participants. This ensures that an efficient meeting location is selected by suggesting the optimal meeting location, taking into account the geographical location information of the participants. Geographical location information includes, but is not limited to, GPS data and address information. The optimal meeting location includes, but is not limited to, transportation access and meeting room facilities. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can suggest a meeting location using an AI model that takes the geographical location information of the participants as input and outputs the optimal meeting location.

[0051] The schedule confirmation unit can update participants' calendar information in real time when checking schedules, reflecting the latest schedules. The schedule confirmation unit can, for example, use AI to update participants' calendar information in real time when checking schedules, reflecting the latest schedules. For example, the schedule confirmation unit can update participants' calendar information in real time, reflecting the latest schedules. By updating participants' calendar information in real time and reflecting the latest schedules, the optimal date can be suggested. Real-time updates include, for example, data synchronization methods and update frequency, but are not limited to, such examples. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can update schedules using an AI model that takes participants' calendar information as input and outputs the latest schedule.

[0052] The speech facilitation unit can provide optimal comments by referring to the content of past meetings when facilitating speech. For example, the speech facilitation unit can use AI to provide optimal comments by referring to the content of past meetings when facilitating speech. For example, the speech facilitation unit can provide relevant comments based on the content of past meetings. This allows relevant comments to be provided by referring to the content of past meetings, thus facilitating the smooth progress of the discussion. The content of past meetings includes, but is not limited to, database search methods and content analysis methods. Optimal comments include, but are not limited to, comments related to the agenda and comments based on the speaker's area of ​​expertise. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments using an AI model that takes the content of past meetings as input and outputs optimal comments.

[0053] The speech facilitation unit can apply different speech facilitation algorithms depending on the progress of the meeting when facilitating speech. For example, the speech facilitation unit can use AI to apply different speech facilitation algorithms depending on the progress of the meeting when facilitating speech. For example, if the meeting is behind schedule, the speech facilitation unit can apply an algorithm that speeds up the discussion. By applying different speech facilitation algorithms depending on the progress of the meeting, the discussion can proceed effectively. Speech facilitation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can apply an algorithm using an AI model that takes the progress of the meeting as input and outputs a speech facilitation algorithm.

[0054] The speech facilitation unit can provide optimal comments based on the participants' areas of expertise and positions when facilitating discussion. For example, the speech facilitation unit can use AI to provide optimal comments based on the participants' areas of expertise and positions when facilitating discussion. For example, the speech facilitation unit can provide relevant comments based on the participants' areas of expertise. This allows the discussion to progress effectively by providing optimal comments based on the participants' areas of expertise and positions. The participants' areas of expertise and positions include, but are not limited to, job descriptions and past statements. Optimal comments include, but are not limited to, comments related to the agenda and comments based on the speaker's area of ​​expertise. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments using an AI model that takes the participants' areas of expertise and positions as input and outputs optimal comments.

[0055] The speech facilitation unit can provide up-to-date information related to the meeting topic in real time when facilitating speech. The speech facilitation unit can, for example, use AI to provide up-to-date information related to the meeting topic in real time when facilitating speech. For example, the speech facilitation unit can provide the latest news related to the meeting topic in real time. By providing up-to-date information related to the meeting topic in real time, the discussion will be based on the latest information. Up-to-date information includes, but is not limited to, analysis of news articles and reference to industry reports. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide information using an AI model that takes the meeting topic as input and outputs up-to-date information.

[0056] The minutes creation unit can automatically suggest the optimal format when creating minutes by referring to past minutes. For example, the minutes creation unit can use a generative AI to automatically suggest the optimal format when creating minutes by referring to past minutes. For example, the minutes creation unit can automatically suggest relevant formats based on past minutes. This streamlines the minutes creation process by automatically suggesting the optimal format by referring to past minutes. Past minutes include, but are not limited to, database search methods and minutes similarity evaluations. Optimal formats include, but are not limited to, agenda importance and relevance evaluation methods. Some or all of the above processing in the minutes creation unit may be performed using generative AI or not. For example, the minutes creation unit can suggest a format using a generative AI model that takes past minutes as input and outputs the optimal format.

[0057] The minutes creation unit can apply different minutes generation algorithms depending on the importance of the meeting when creating minutes. For example, the minutes creation unit can use a generative AI to apply different minutes generation algorithms depending on the importance of the meeting when creating minutes. For example, if the meeting is of high importance, the minutes creation unit can apply an algorithm that generates detailed minutes. In this way, by applying different minutes generation algorithms depending on the importance of the meeting, minutes appropriate to the importance are generated. The minutes generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the minutes creation unit may be performed using a generative AI or not. For example, the minutes creation unit can generate minutes using a generative AI model that takes the importance of the meeting as input and outputs minutes.

[0058] The minutes creation unit can customize the content of the meeting minutes based on the frequency of participation in the meeting. For example, the minutes creation unit can use a generative AI to customize the content of the meeting minutes based on the frequency of participation in the meeting. For example, the minutes creation unit can include relevant content in the minutes based on the frequency of participation in the participants. This ensures that important content is included in the minutes by customizing the content based on the frequency of participation in the meeting. Frequency of participation includes, but is not limited to, the number of times a statement is made and the length of a statement. Content of the minutes includes, but is not limited to, the main points of a statement and the names of the speakers. Some or all of the above processing in the minutes creation unit may be performed using a generative AI or not. For example, the minutes creation unit can customize the content using a generative AI model that takes the frequency of participation in the participants as input and outputs the content of the meeting minutes.

[0059] The minutes creation unit can automatically attach reference materials related to the meeting topic when creating the minutes. For example, the minutes creation unit can use a generative AI to automatically attach reference materials related to the meeting topic when creating the minutes. For example, the minutes creation unit can automatically attach reference materials related to the meeting topic. This enriches the content of the minutes by automatically attaching reference materials related to the meeting topic. Reference materials include, but are not limited to, external resources, literature, data, and statistical information. Some or all of the above processing in the minutes creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the minutes creation unit can attach materials using a generative AI model that takes the meeting topic as input and outputs reference materials.

[0060] The email delivery unit can suggest the optimal delivery timing by referring to past email delivery history when sending emails. For example, the email delivery unit can use AI to suggest the optimal delivery timing by referring to past email delivery history when sending emails. For example, the email delivery unit suggests the optimal delivery timing based on past email delivery history. By suggesting the optimal delivery timing by referring to past email delivery history, the email open rate can be improved. Past email delivery history includes, but is not limited to, database search methods and delivery timing evaluation methods. Optimal delivery timing includes, but is not limited to, selection of delivery time and delivery date. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can suggest timing using an AI model that takes past email delivery history as input and outputs the optimal delivery timing.

[0061] The email distribution unit can apply different email distribution algorithms depending on the importance of the meeting when sending emails. For example, the email distribution unit can use AI to apply different email distribution algorithms depending on the importance of the meeting when sending emails. For example, if the meeting is of high importance, the email distribution unit can apply an algorithm that sends a detailed email. By applying different email distribution algorithms depending on the importance of the meeting, information about important meetings is reliably conveyed. Email distribution algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the email distribution unit may be performed using AI or not. For example, the email distribution unit can send emails using an AI model that takes the importance of the meeting as input and outputs an email.

[0062] The email delivery unit can analyze participants' email open history when sending emails and determine the optimal delivery timing. For example, the email delivery unit can use AI to analyze participants' email open history and determine the optimal delivery timing when sending emails. For example, the email delivery unit can determine the optimal delivery timing based on participants' email open history. By analyzing participants' email open history and determining the optimal delivery timing, the email open rate can be improved. Email open history includes, but is not limited to, examples such as open time and open frequency. Optimal delivery timing includes, but is not limited to, examples such as selection of delivery time and delivery date. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can determine the timing using an AI model that takes participants' email open history as input and outputs the optimal delivery timing.

[0063] The email distribution unit can automatically attach additional information related to the meeting topic when sending emails. For example, the email distribution unit can use AI to automatically attach additional information related to the meeting topic when sending emails. This allows participants to quickly obtain the necessary information by automatically attaching additional information related to the meeting topic. This additional information may include, but is not limited to, external resources, literature, data, and statistics. Some or all of the above processing in the email distribution unit may be performed using AI or not. For example, the email distribution unit can attach information using an AI model that takes the meeting topic as input and outputs additional information.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The meeting preparation efficiency system can also include an agenda customization section that customizes agenda items based on the participants' areas of expertise. This section automatically suggests relevant agenda items based on the participants' fields of expertise. For example, if there are many participants from the technical department, technical topics can be prioritized. Similarly, if there are many participants from the marketing department, topics related to marketing strategy can be suggested. This provides an agenda tailored to the participants' areas of expertise, improving meeting efficiency. Furthermore, the agenda customization section can analyze past statements made by participants and suggest relevant topics. For example, revisiting themes frequently discussed in past meetings can facilitate continued discussion.

[0066] The meeting preparation efficiency system can also include an industry trend integration unit that automatically incorporates the latest industry trends related to the meeting topic. This unit automatically collects the latest news and reports related to the meeting topic and reflects them in the agenda. For example, in the case of a meeting about new product development, it can incorporate the latest market trends and competitor activities. Furthermore, the industry trend integration unit can collect expert opinions and analyses related to the meeting topic and reflect them in the agenda. This ensures that discussions are based on the latest information, improving the quality of the meeting.

[0067] The meeting preparation efficiency system can also include a document collection unit that automatically gathers and presents relevant materials and data to the user when the meeting theme and objectives are entered. The document collection unit automatically collects and displays relevant past meeting materials and data based on the meeting theme and objectives. For example, in the case of a meeting about new product development, it can automatically collect and present past market research results and development schedules. The document collection unit can also search external resources and literature to gather relevant information. This streamlines meeting preparation and allows for quick access to necessary information.

[0068] The meeting preparation efficiency system can further include a speech algorithm application unit that applies different speech facilitation algorithms depending on the progress of the meeting. The speech algorithm application unit applies the most suitable speech facilitation algorithm according to the progress of the meeting. For example, if the meeting is behind schedule, an algorithm that quickly advances the discussion can be applied. Conversely, if the meeting is progressing smoothly, an algorithm that encourages detailed discussion can be applied. This ensures that optimal speech facilitation is performed according to the progress of the meeting, and the discussion proceeds effectively.

[0069] The meeting preparation efficiency system can also include a meeting minutes format suggestion unit that automatically proposes the optimal format when creating meeting minutes by referring to past meeting minutes. The meeting minutes format suggestion unit automatically proposes relevant formats based on past meeting minutes. For example, it can suggest relevant formats based on formats used in past meetings. Furthermore, the meeting minutes format suggestion unit can also propose the optimal format according to the meeting's theme and purpose. This streamlines meeting minute creation and ensures that important information is recorded without omission.

[0070] The meeting preparation efficiency system can also include an email timing suggestion unit that proposes the optimal delivery time by referring to past email delivery history. The email timing suggestion unit proposes the optimal delivery time based on past email delivery history. For example, it can analyze past email delivery history and suggest the time slot with the highest open rate. Furthermore, the email timing suggestion unit can determine the optimal delivery time based on participants' email open history. This improves email open rates and streamlines information sharing.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk inputs the meeting theme and purpose. The reception desk provides an interface for users to input the meeting theme and purpose, allowing them to input the theme and purpose using text or voice input. For example, speech recognition technology can be used to convert the user's voice into text and input it as the meeting theme and purpose. Step 2: The agenda creation department automatically generates a draft agenda based on the information entered by the reception department. The agenda creation department uses a generation AI to generate a draft agenda based on the meeting's theme and objectives. For example, if the meeting's theme is "New Product Development," it automatically creates agenda items such as "Market Research Results Report," "Development Schedule Confirmation," and "Budget Review." Step 3: The schedule confirmation unit checks participants' schedules and presents possible dates. Based on participants' calendar information, the schedule confirmation unit proposes the most suitable meeting date. For example, by automatically generating candidate dates that take everyone's availability into consideration, scheduling can be made smoothly. Step 4: The discussion facilitation unit encourages participation during the meeting and provides summaries and closing comments. The discussion facilitation unit uses generative AI to provide comments that encourage participation and support the progress of the discussion in real time. For example, if the discussion stalls, it will provide comments such as, "Let's move on to the next agenda item." It also presents summaries of the content and provides closing comments in accordance with the progress of the meeting. Step 5: The minutes creation department automatically generates meeting minutes after the meeting. The minutes creation department uses a generation AI to automatically summarize the content of the discussions during the meeting and create the minutes. For example, it extracts the important points and decisions made during the meeting and compiles them into the minutes. Step 6: The email distribution department distributes the meeting minutes via email. The email distribution department uses generation AI to automatically create emails based on the generated meeting minutes and distributes them to participants. For example, it automatically compiles the meeting minutes, including important points and decisions, into an email and distributes it to participants.

[0073] (Example of form 2) The meeting preparation efficiency system according to an embodiment of the present invention is a system that utilizes generative AI to streamline meeting preparation. This meeting preparation efficiency system automatically creates a draft agenda before the meeting, checks participants' schedules, and presents possible dates. During the meeting, the generative AI encourages participation and provides summaries of the content and closing comments. After the meeting, the generative AI automatically creates meeting minutes and distributes them via email. This mechanism supports and streamlines the entire meeting operation. For example, the generative AI automatically creates a draft agenda before the meeting. When a user inputs the theme and purpose of the meeting, the generative AI generates a draft agenda based on that. For example, if the theme of the meeting is "new product development," the generative AI automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." This significantly reduces the time required for meeting preparation. Next, the generative AI checks participants' schedules and presents possible dates. The generative AI proposes the optimal meeting date based on the participants' calendar information. For example, by automatically providing candidate dates that take everyone's availability into consideration, scheduling can be adjusted smoothly. This allows for the rapid scheduling of meetings. During the meeting, the generating AI encourages participation and provides summaries and closing comments. The generating AI provides comments in real time to encourage participation and support the progress of the discussion. For example, if the discussion stalls, the generating AI will provide a comment such as, "Let's move on to the next agenda item." It also supports the efficient conclusion of the meeting on time by presenting summaries of the content and providing closing comments in accordance with the progress of the meeting. After the meeting, the generating AI automatically creates meeting minutes and distributes them via email. The generating AI automatically summarizes the content of the discussion during the meeting and creates the minutes. For example, it extracts the important points and decisions of the meeting and compiles them into minutes. Furthermore, the generating AI automatically creates emails and distributes the minutes to participants. This allows for rapid information sharing and smooth follow-up after the meeting. In this way, by utilizing the generating AI, the entire process from meeting preparation to progress and follow-up can be streamlined and handled without any omissions or errors.This provides support for all aspects of meeting management and improves operational efficiency. The meeting preparation efficiency system streamlines the entire process, from preparation to execution and follow-up, ensuring no omissions or errors are made.

[0074] The meeting preparation efficiency system according to this embodiment comprises a reception unit, an agenda creation unit, a schedule confirmation unit, a discussion facilitation unit, a meeting minutes creation unit, and an email distribution unit. The reception unit inputs the theme and purpose of the meeting. The reception unit provides, for example, an interface for the user to input the theme and purpose of the meeting. For example, the reception unit allows the user to input the theme and purpose of the meeting in text format. The reception unit also allows the user to input the theme and purpose of the meeting using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text and inputs it as the theme and purpose of the meeting. The agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit. The agenda creation unit generates a draft agenda based on the theme and purpose of the meeting, for example, using generation AI. For example, if the theme of the meeting is "new product development," the agenda creation unit automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." The schedule confirmation unit checks the participants' schedules and presents possible dates. The Schedule Confirmation Unit, for example, suggests the optimal meeting date based on participants' calendar information. For example, the Schedule Confirmation Unit automatically generates candidate dates that take everyone's availability into consideration, facilitating smooth scheduling. The Speech Facilitation Unit encourages participation during the meeting and provides summaries of the content and closing comments. For example, the Speech Facilitation Unit uses generational AI to provide comments that encourage participation in real time and comments that support the progress of the discussion. For example, if the discussion stalls, the Speech Facilitation Unit provides comments such as, "Let's move on to the next agenda item." The Speech Facilitation Unit also presents summaries of the content and provides closing comments in accordance with the progress of the meeting. The Minutes Creation Unit automatically creates meeting minutes after the meeting. For example, the Minutes Creation Unit uses generational AI to automatically summarize the content of the discussion during the meeting and create the minutes. For example, the Minutes Creation Unit extracts important points and decisions from the meeting and compiles them into minutes. The Email Distribution Unit distributes the meeting minutes via email. The email distribution department, for example, uses generation AI to automatically create emails based on the generated meeting minutes and distributes them to participants.For example, the email distribution department automatically compiles meeting minutes, including key points and decisions, into emails and distributes them to participants. This allows the meeting preparation efficiency system, according to this embodiment, to streamline the entire process from meeting preparation to execution and follow-up, ensuring no information is missed or overlooked.

[0075] The reception desk inputs the meeting theme and purpose. For example, the reception desk provides an interface for users to input the meeting theme and purpose. Specifically, it features an intuitive graphical user interface (GUI) that allows users to easily input information using text boxes and dropdown menus. The reception desk also allows users to input the meeting theme and purpose using voice input. Using speech recognition technology, it converts the user's voice into text and inputs it as the meeting theme or purpose. For example, if a user voice-inputs, "The theme of the next meeting is new product development," the system converts this into text and registers it as the meeting theme. Furthermore, the reception desk also has a function to automatically suggest similar themes and purposes by referring to past meeting data. This allows users to efficiently set themes and purposes while referring to past meeting content. For example, if a meeting on "new product development" was held in the past, the system will refer to the minutes and agenda and automatically suggest relevant information. In this way, the reception desk supports users in quickly and accurately inputting meeting themes and purposes.

[0076] The agenda creation department automatically generates a draft agenda based on information entered by the reception department. For example, using a generative AI, the agenda creation department generates a draft agenda based on the meeting's theme and objectives. Specifically, the generative AI learns from past meeting data and relevant literature to suggest optimal agenda items. For example, if the meeting theme is "New Product Development," it automatically creates agenda items such as "Market Research Results Report," "Development Schedule Confirmation," and "Budget Review." The generative AI uses natural language processing technology to analyze the theme and objectives entered by the user and extract relevant keywords and topics. This allows the agenda creation department to generate a draft agenda that accurately reflects the user's intentions. Furthermore, the agenda creation department presents the generated draft agenda to the user and provides an interface that allows for modifications and additions as needed. For example, if a user wants to delete the "Market Research Results Report" item, they can easily do so. The agenda creation department also has a function to update the agenda in real time according to the progress of the meeting. This allows the agenda creation section to support users in efficiently creating meeting agendas and adapting to changes flexibly.

[0077] The schedule confirmation unit checks participants' schedules and presents potential dates. For example, it suggests the optimal meeting date based on participants' calendar information. Specifically, the schedule confirmation unit collects each participant's calendar information and analyzes their availability. This allows for smooth scheduling by automatically generating candidate dates that consider everyone's availability. For example, the schedule confirmation unit extracts available time slots from each participant's calendar and presents non-overlapping time slots as candidate dates. The schedule confirmation unit also has a function to suggest the optimal date considering the priority and importance of each participant. For example, it prioritizes the schedules of important participants and adjusts the schedules of other participants to match their availability. Furthermore, the schedule confirmation unit can learn the frequency of meetings and past scheduling patterns to suggest the optimal meeting time. As a result, the schedule confirmation unit can efficiently and effectively schedule meetings and suggest dates that are easy for all participants to attend.

[0078] The speech facilitation unit encourages participation during meetings and provides summaries and closing comments. For example, it uses a generative AI to provide real-time comments encouraging participation and supporting the flow of discussion. Specifically, the generative AI analyzes the content of discussions and generates comments encouraging participation at appropriate times. For instance, if the discussion stalls, it might provide a comment such as, "Let's move on to the next topic." The speech facilitation unit also presents summaries of the content and provides closing comments in line with the progress of the meeting. The generative AI analyzes the content of discussions in real time, extracting key points and conclusions. This allows the speech facilitation unit to facilitate smooth meeting progress and support participants in efficiently advancing the discussion. Furthermore, the speech facilitation unit monitors the frequency and content of participants' contributions and provides an environment that encourages specific participants to speak. For example, if a particular participant is not speaking, it provides a comment encouraging them to participate. This allows the speech facilitation unit to create a meeting environment where everyone can actively participate, improving the quality of the discussion.

[0079] The meeting minutes creation unit automatically generates meeting minutes after the meeting. For example, it uses a generation AI to automatically summarize the content of the meeting and create the minutes. Specifically, the generation AI converts the audio data from the meeting into text and extracts important points and decisions. This allows the meeting minutes creation unit to accurately record the important points and decisions of the meeting and compile them into minutes. Furthermore, the meeting minutes creation unit presents the generated minutes to the user and provides an interface that allows for modifications and additions as needed. For example, if a user wants to add a specific statement, they can easily do so. The meeting minutes creation unit also has a function to update the minutes in real time according to the progress of the meeting. This allows the meeting minutes creation unit to support users in efficiently creating minutes and responding flexibly. Additionally, the meeting minutes creation unit has a function to refer to past meeting minutes data and automatically suggest similar meeting content. This allows users to efficiently create minutes while referring to past meeting content.

[0080] The email distribution department distributes meeting minutes via email. For example, it uses a generation AI to automatically create emails based on the generated meeting minutes and distribute them to participants. Specifically, the generation AI extracts key points and decisions from the minutes and generates a concise email body. This allows the email distribution department to automatically summarize meeting minutes, including key points and decisions, into emails and distribute them to participants. Furthermore, the email distribution department monitors email delivery status and sends reminders to participants who haven't read the emails. For example, if an email is not opened within a certain period, it automatically sends a reminder to ensure important information is conveyed. The email distribution department also provides an interface to manage recipient lists, allowing for additions and deletions as needed. This enables the email distribution department to efficiently and reliably distribute meeting minutes, supporting all participants in sharing important information. Additionally, the email distribution department archives the content of distributed emails for later reference. This allows for easy searching of past meeting content and quick retrieval of necessary information.

[0081] The agenda creation unit can generate a draft agenda based on the meeting's theme and objectives. For example, the agenda creation unit uses generative AI to generate a draft agenda based on the meeting's theme and objectives. For example, if the meeting's theme is "new product development," the agenda creation unit automatically creates agenda items such as "market research results report," "development schedule confirmation," and "budget review." This streamlines meeting preparation by generating a draft agenda based on the meeting's theme and objectives. The draft agenda may include, but is not limited to, a list of topics, time allocation, and assigned personnel. Some or all of the above-described processes in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can generate a draft agenda using a generative AI model that takes the meeting's theme and objectives as input and outputs a draft agenda.

[0082] The schedule confirmation unit can suggest the optimal meeting date based on the participants' calendar information. For example, the schedule confirmation unit can suggest the optimal meeting date based on the participants' calendar information. For example, the schedule confirmation unit can automatically generate candidate dates that take everyone's availability into consideration, thereby facilitating smooth schedule adjustments. This streamlines schedule adjustments by suggesting the optimal meeting date based on the participants' calendar information. The optimal meeting date may include, but is not limited to, everyone's free time and high-priority dates. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can suggest a meeting date using an AI model that takes participants' calendar information as input and outputs the optimal meeting date.

[0083] The speech facilitation unit can provide comments that encourage participation in real time. For example, the speech facilitation unit can provide comments that encourage participation in real time using generative AI. For example, if the discussion stalls, the speech facilitation unit can provide a comment such as, "Let's move on to the next topic." By providing comments that encourage participation in real time, the meeting proceeds more smoothly. Comments that encourage participation include, but are not limited to, asking questions or soliciting opinions. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments that encourage participation using an AI model that takes the progress of the meeting as input and outputs comments that encourage participation.

[0084] The speech facilitation unit can present a summary of the content and provide closing comments in accordance with the progress of the meeting. The speech facilitation unit can, for example, use generative AI to present a summary of the content and provide closing comments in accordance with the progress of the meeting. For example, the speech facilitation unit can present a summary of the content and provide closing comments in accordance with the progress of the meeting. This allows the meeting to conclude efficiently by presenting a summary of the content and providing closing comments in accordance with the progress of the meeting. The content summary may include, for example, how to extract key points and the length of the summary. The closing comments may include, for example, a summary of the meeting and action items for the next meeting. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide a summary of the content and closing comments using an AI model that takes the progress of the meeting as input and outputs a summary of the content and closing comments.

[0085] The minutes creation unit can automatically summarize the content of discussions during a meeting and create meeting minutes. For example, the minutes creation unit can use a generative AI to automatically summarize the content of discussions during a meeting and create meeting minutes. For example, the minutes creation unit can extract important points and decisions made during a meeting and summarize them as minutes. This streamlines the minutes creation process by automatically summarizing the content of discussions during a meeting. The content of discussions during a meeting includes, but is not limited to, the main points of the discussion and the names of the speakers. Some or all of the above-described processes in the minutes creation unit may be performed using a generative AI, or they may not. For example, the minutes creation unit can create meeting minutes using a generative AI model that takes the content of discussions during a meeting as input and outputs meeting minutes.

[0086] The email distribution unit can automatically create and distribute emails to participants based on the generated meeting minutes. For example, the email distribution unit can use generation AI to automatically create and distribute emails to participants based on the generated meeting minutes. For example, the email distribution unit can automatically summarize the meeting minutes, including important points and decisions, into an email and distribute it to participants. This allows for rapid information sharing by automatically creating and distributing emails based on the generated meeting minutes. Automatic email creation includes, but is not limited to, email templates and delivery timing. Some or all of the above processes in the email distribution unit may be performed using AI or not. For example, the email distribution unit can create emails using an AI model that takes the generated meeting minutes as input and outputs emails.

[0087] The reception desk can estimate the user's emotions and adjust the input method for meeting themes and objectives based on the estimated emotions. The reception desk estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception desk analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the input method based on the user's emotions, it is possible to reduce user stress and enable efficient input. User emotions include, but are not limited to, joy, sadness, and anger. Adjusting the input method includes, but is not limited to, modifying input forms or providing input guides. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust the input method using an AI model that takes user emotion data as input and outputs an input method.

[0088] The reception desk can refer to past meeting themes and objectives and automatically suggest similar themes. For example, the reception desk can use AI to refer to past meeting themes and objectives and automatically suggest similar themes. For example, the reception desk can automatically display similar themes as candidates based on meeting themes previously entered by the user. This allows users to efficiently set meeting themes and objectives by referring to past meeting themes and objectives. Past meeting themes and objectives include, but are not limited to, database search methods and similarity calculation methods. Similar themes include, but are not limited to, keyword matching and theme relevance. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest similar themes using an AI model that takes past meeting themes and objectives as input and outputs similar themes.

[0089] The reception desk can automatically collect and present relevant materials and data to the user when the meeting theme and purpose are entered. For example, the reception desk can use AI to automatically collect and present relevant materials and data to the user when the meeting theme and purpose are entered. For example, when the user enters the meeting theme, the reception desk automatically collects and displays relevant past meeting materials. This streamlines meeting preparation by automatically collecting and presenting relevant materials and data to the user. Relevant materials and data include, but are not limited to, database search methods and material selection criteria. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can collect materials and data using an AI model that takes the meeting theme and purpose as input and outputs relevant materials and data.

[0090] The reception unit can estimate the user's emotions and determine the priority of the input themes and objectives based on the estimated user emotions. The reception unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the reception unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By determining priorities based on the user's emotions, important themes and objectives are processed preferentially. User emotions include, but are not limited to, joy, sadness, and anger. Determining priorities includes, but are not limited to, criteria for evaluating importance and methods for determining urgency. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can determine priorities using an AI model that takes user emotion data as input and outputs priorities.

[0091] The reception desk can analyze the user's past speaking history and automatically complete relevant keywords when the user inputs the meeting theme and purpose. For example, the reception desk can use AI to analyze the user's past speaking history and automatically complete relevant keywords when the user inputs the meeting theme and purpose. For example, the reception desk can automatically complete relevant themes based on keywords the user has previously spoken. This streamlines the input process by analyzing the user's past speaking history and automatically completing relevant keywords. Past speaking history includes, but is not limited to, database search methods and methods for analyzing spoken content. Relevant keywords include, but are not limited to, keyword matching rates and relevance evaluation methods. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can automatically complete keywords using an AI model that takes the user's past speaking history as input and outputs relevant keywords.

[0092] The reception desk can provide an optimal input format based on the user's job duties and position when inputting the meeting theme and purpose. For example, the reception desk can use AI to provide an optimal input format based on the user's job duties and position when inputting the meeting theme and purpose. For example, the reception desk can automatically suggest relevant themes and purposes based on the user's job duties. This streamlines input by providing an optimal input format based on the user's job duties and position. Job duties and position include, but are not limited to, job descriptions and hierarchical positions. An optimal input format includes, but are not limited to, the selection of input items and the provision of input guides. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can provide an input format using an AI model that takes the user's job duties and position as input and outputs an optimal input format.

[0093] The agenda creation unit can estimate the user's emotions and adjust the way the agenda is presented based on the estimated user emotions. The agenda creation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the agenda creation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the way the agenda is presented based on the user's emotions, an agenda that is easy for the user to understand is provided. User emotions include, but are not limited to, joy, sadness, and anger. Ways to present the agenda include, but are not limited to, changes in wording and visual emphasis. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can adjust the way the agenda is presented using a generative AI model that takes user emotion data as input and outputs a way to present the agenda.

[0094] The agenda creation unit can automatically suggest optimal items when creating an agenda by referring to past meeting agendas. For example, the agenda creation unit can use generative AI to automatically suggest optimal items when creating an agenda by referring to past meeting agendas. For example, the agenda creation unit can automatically suggest relevant items based on past meeting agendas. This streamlines agenda creation by automatically suggesting optimal items by referring to past meeting agendas. Past meeting agendas include, but are not limited to, database search methods and agenda similarity evaluation methods. Optimal items include, but are not limited to, agenda importance and relevance evaluation methods. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can automatically suggest items using a generative AI model that takes past meeting agendas as input and outputs optimal items.

[0095] The agenda creation unit can apply different agenda generation algorithms depending on the purpose of the meeting when creating an agenda. For example, the agenda creation unit can use a generative AI to apply different agenda generation algorithms depending on the purpose of the meeting when creating an agenda. For example, if the purpose of the meeting is information sharing, the agenda creation unit will generate an agenda that organizes the information. In this way, by applying different agenda generation algorithms depending on the purpose of the meeting, an agenda that suits the purpose will be generated. Agenda generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the agenda creation unit may be performed using a generative AI or not. For example, the agenda creation unit can generate an agenda using a generative AI model that takes the purpose of the meeting as input and outputs an agenda.

[0096] The agenda creation unit can estimate the user's emotions and adjust the level of detail in the agenda based on the estimated emotions. The agenda creation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the agenda creation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the level of detail in the agenda based on the user's emotions, the optimal agenda for the user is provided. User emotions include, but are not limited to, joy, sadness, and anger. The level of detail in the agenda includes, but is not limited to, adding detailed explanations or listing only the key points. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can adjust the level of detail in the agenda using a generative AI model that takes user emotion data as input and outputs the level of detail in the agenda.

[0097] The agenda creation unit can customize agenda items based on the expertise of meeting participants when creating an agenda. For example, the agenda creation unit can use generative AI to customize agenda items based on the expertise of meeting participants when creating an agenda. For example, the agenda creation unit can automatically suggest relevant agenda items based on the participants' expertise. This ensures that an optimal agenda is provided for participants by customizing agenda items based on their expertise. The participants' expertise includes, but is not limited to, job descriptions and past statements. Customizing agenda items includes, but is not limited to, adding topics related to expertise or deleting unnecessary topics. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can customize agenda items using a generative AI model that takes participants' expertise as input and outputs agenda items.

[0098] The agenda creation unit can automatically incorporate the latest industry trends related to the meeting topic when creating the agenda. For example, the agenda creation unit can use generative AI to automatically incorporate the latest industry trends related to the meeting topic when creating the agenda. For example, the agenda creation unit can automatically incorporate the latest industry news related to the meeting topic. This ensures that the agenda is based on the latest information by automatically incorporating the latest industry trends related to the meeting topic. Latest industry trends include, but are not limited to, analysis of news articles and reference to industry reports. Some or all of the above processing in the agenda creation unit may be performed using generative AI or not. For example, the agenda creation unit can incorporate industry trends using a generative AI model that takes the meeting topic as input and outputs the latest industry trends.

[0099] The schedule confirmation unit can estimate the user's emotions and determine the priority of schedule candidates based on the estimated user emotions. The schedule confirmation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the schedule confirmation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By determining the priority of schedule candidates based on the user's emotions, important dates are displayed preferentially. User emotions include, but are not limited to, joy, sadness, and anger. The priority of schedule candidates includes, but are not limited to, criteria for evaluating importance and methods for determining urgency. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can determine the priority using an AI model that takes user emotion data as input and outputs the priority of schedule candidates.

[0100] The schedule confirmation unit can suggest the optimal meeting location when confirming the schedule, taking into account the geographical location information of the participants. For example, the schedule confirmation unit can use AI to suggest the optimal meeting location when confirming the schedule, taking into account the geographical location information of the participants. For example, the schedule confirmation unit suggests the optimal meeting location based on the geographical location information of the participants. This ensures that an efficient meeting location is selected by suggesting the optimal meeting location, taking into account the geographical location information of the participants. Geographical location information includes, but is not limited to, GPS data and address information. The optimal meeting location includes, but is not limited to, transportation access and meeting room facilities. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can suggest a meeting location using an AI model that takes the geographical location information of the participants as input and outputs the optimal meeting location.

[0101] The schedule confirmation unit can update participants' calendar information in real time when checking schedules, reflecting the latest schedules. The schedule confirmation unit can, for example, use AI to update participants' calendar information in real time when checking schedules, reflecting the latest schedules. For example, the schedule confirmation unit can update participants' calendar information in real time, reflecting the latest schedules. By updating participants' calendar information in real time and reflecting the latest schedules, the optimal date can be suggested. Real-time updates include, for example, data synchronization methods and update frequency, but are not limited to, such examples. Some or all of the above processing in the schedule confirmation unit may be performed using AI or not. For example, the schedule confirmation unit can update schedules using an AI model that takes participants' calendar information as input and outputs the latest schedule.

[0102] The speech facilitation unit can estimate the user's emotions and adjust speech facilitation comments based on the estimated user emotions. The speech facilitation unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, the speech facilitation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting speech facilitation comments based on the user's emotions, the unit enables the user to speak in a relaxed manner. User emotions include, but are not limited to, joy, sadness, and anger. Speech facilitation comments include, but are not limited to, asking questions or soliciting opinions. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can adjust comments using an AI model that takes user emotion data as input and outputs speech facilitation comments.

[0103] The speech facilitation unit can provide optimal comments by referring to the content of past meetings when facilitating speech. For example, the speech facilitation unit can use AI to provide optimal comments by referring to the content of past meetings when facilitating speech. For example, the speech facilitation unit can provide relevant comments based on the content of past meetings. This allows relevant comments to be provided by referring to the content of past meetings, thus facilitating the smooth progress of the discussion. The content of past meetings includes, but is not limited to, database search methods and content analysis methods. Optimal comments include, but are not limited to, comments related to the agenda and comments based on the speaker's area of ​​expertise. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments using an AI model that takes the content of past meetings as input and outputs optimal comments.

[0104] The speech facilitation unit can apply different speech facilitation algorithms depending on the progress of the meeting when facilitating speech. For example, the speech facilitation unit can use AI to apply different speech facilitation algorithms depending on the progress of the meeting when facilitating speech. For example, if the meeting is behind schedule, the speech facilitation unit can apply an algorithm that speeds up the discussion. By applying different speech facilitation algorithms depending on the progress of the meeting, the discussion can proceed effectively. Speech facilitation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can apply an algorithm using an AI model that takes the progress of the meeting as input and outputs a speech facilitation algorithm.

[0105] The speech facilitation unit can estimate the user's emotions and adjust the timing of speech facilitation based on the estimated user emotions. The speech facilitation unit estimates the user's emotions using, for example, an emotion engine or a generative AI. For example, the speech facilitation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the timing of speech facilitation based on the user's emotions, speech is encouraged at the appropriate time. User emotions include, but are not limited to, joy, sadness, and anger. Timing of speech facilitation includes, but are not limited to, the interval between speeches and the progress of the meeting. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can adjust the timing using an AI model that takes user emotion data as input and outputs the timing of speech facilitation.

[0106] The speech facilitation unit can provide optimal comments based on the participants' areas of expertise and positions when facilitating discussion. For example, the speech facilitation unit can use AI to provide optimal comments based on the participants' areas of expertise and positions when facilitating discussion. For example, the speech facilitation unit can provide relevant comments based on the participants' areas of expertise. This allows the discussion to progress effectively by providing optimal comments based on the participants' areas of expertise and positions. The participants' areas of expertise and positions include, but are not limited to, job descriptions and past statements. Optimal comments include, but are not limited to, comments related to the agenda and comments based on the speaker's area of ​​expertise. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide comments using an AI model that takes the participants' areas of expertise and positions as input and outputs optimal comments.

[0107] The speech facilitation unit can provide up-to-date information related to the meeting topic in real time when facilitating speech. The speech facilitation unit can, for example, use AI to provide up-to-date information related to the meeting topic in real time when facilitating speech. For example, the speech facilitation unit can provide the latest news related to the meeting topic in real time. By providing up-to-date information related to the meeting topic in real time, the discussion will be based on the latest information. Up-to-date information includes, but is not limited to, analysis of news articles and reference to industry reports. Some or all of the above processing in the speech facilitation unit may be performed using AI or not. For example, the speech facilitation unit can provide information using an AI model that takes the meeting topic as input and outputs up-to-date information.

[0108] The minutes creation unit can estimate the user's emotions and adjust the presentation of the minutes based on the estimated emotions. The minutes creation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the minutes creation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the presentation of the minutes based on the user's emotions, the minutes are provided in a way that is easy for the user to understand. User emotions include, but are not limited to, joy, sadness, and anger. Presentation of the minutes includes, but are not limited to, changes in wording and visual emphasis. Some or all of the above processing in the minutes creation unit may be performed using generative AI or not. For example, the minutes creation unit can adjust the presentation using a generative AI model that takes user emotion data as input and outputs a presentation of the minutes.

[0109] The minutes creation unit can automatically suggest the optimal format when creating minutes by referring to past minutes. For example, the minutes creation unit can use a generative AI to automatically suggest the optimal format when creating minutes by referring to past minutes. For example, the minutes creation unit can automatically suggest relevant formats based on past minutes. This streamlines the minutes creation process by automatically suggesting the optimal format by referring to past minutes. Past minutes include, but are not limited to, database search methods and minutes similarity evaluations. Optimal formats include, but are not limited to, agenda importance and relevance evaluation methods. Some or all of the above processing in the minutes creation unit may be performed using generative AI or not. For example, the minutes creation unit can suggest a format using a generative AI model that takes past minutes as input and outputs the optimal format.

[0110] The minutes creation unit can apply different minutes generation algorithms depending on the importance of the meeting when creating minutes. For example, the minutes creation unit can use a generative AI to apply different minutes generation algorithms depending on the importance of the meeting when creating minutes. For example, if the meeting is of high importance, the minutes creation unit can apply an algorithm that generates detailed minutes. In this way, by applying different minutes generation algorithms depending on the importance of the meeting, minutes appropriate to the importance are generated. The minutes generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the minutes creation unit may be performed using a generative AI or not. For example, the minutes creation unit can generate minutes using a generative AI model that takes the importance of the meeting as input and outputs minutes.

[0111] The minutes creation unit can estimate the user's emotions and adjust the level of detail in the minutes based on the estimated emotions. The minutes creation unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the minutes creation unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the level of detail in the minutes based on the user's emotions, the unit provides the user with the most suitable minutes. User emotions include, but are not limited to, joy, sadness, and anger. The level of detail in the minutes includes, but is not limited to, adding detailed explanations or including only the main points. Some or all of the above processing in the minutes creation unit may be performed using generative AI or not. For example, the minutes creation unit can adjust the level of detail using a generative AI model that takes user emotion data as input and outputs the level of detail in the minutes.

[0112] The minutes creation unit can customize the content of the meeting minutes based on the frequency of participation in the meeting. For example, the minutes creation unit can use a generative AI to customize the content of the meeting minutes based on the frequency of participation in the meeting. For example, the minutes creation unit can include relevant content in the minutes based on the frequency of participation in the participants. This ensures that important content is included in the minutes by customizing the content based on the frequency of participation in the meeting. Frequency of participation includes, but is not limited to, the number of times a statement is made and the length of a statement. Content of the minutes includes, but is not limited to, the main points of a statement and the names of the speakers. Some or all of the above processing in the minutes creation unit may be performed using a generative AI or not. For example, the minutes creation unit can customize the content using a generative AI model that takes the frequency of participation in the participants as input and outputs the content of the meeting minutes.

[0113] The minutes creation unit can automatically attach reference materials related to the meeting topic when creating the minutes. For example, the minutes creation unit can use a generative AI to automatically attach reference materials related to the meeting topic when creating the minutes. For example, the minutes creation unit can automatically attach reference materials related to the meeting topic. This enriches the content of the minutes by automatically attaching reference materials related to the meeting topic. Reference materials include, but are not limited to, external resources, literature, data, and statistical information. Some or all of the above processing in the minutes creation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the minutes creation unit can attach materials using a generative AI model that takes the meeting topic as input and outputs reference materials.

[0114] The email delivery unit can estimate the user's emotions and adjust the email's expression based on those emotions. The email delivery unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the email delivery unit analyzes the user's facial expressions using facial recognition technology to estimate their emotions. This allows the email to be adjusted based on the user's emotions, providing an email that is easy for the user to understand. User emotions include, but are not limited to, joy, sadness, and anger. Email expression methods include, but are not limited to, changes in wording and visual emphasis. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can adjust the expression using an AI model that takes user emotion data as input and outputs email expression methods.

[0115] The email delivery unit can suggest the optimal delivery timing by referring to past email delivery history when sending emails. For example, the email delivery unit can use AI to suggest the optimal delivery timing by referring to past email delivery history when sending emails. For example, the email delivery unit suggests the optimal delivery timing based on past email delivery history. By suggesting the optimal delivery timing by referring to past email delivery history, the email open rate can be improved. Past email delivery history includes, but is not limited to, database search methods and delivery timing evaluation methods. Optimal delivery timing includes, but is not limited to, selection of delivery time and delivery date. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can suggest timing using an AI model that takes past email delivery history as input and outputs the optimal delivery timing.

[0116] The email distribution unit can apply different email distribution algorithms depending on the importance of the meeting when sending emails. For example, the email distribution unit can use AI to apply different email distribution algorithms depending on the importance of the meeting when sending emails. For example, if the meeting is of high importance, the email distribution unit can apply an algorithm that sends a detailed email. By applying different email distribution algorithms depending on the importance of the meeting, information about important meetings is reliably conveyed. Email distribution algorithms include, but are not limited to, rule-based and machine learning-based algorithms. Some or all of the above processing in the email distribution unit may be performed using AI or not. For example, the email distribution unit can send emails using an AI model that takes the importance of the meeting as input and outputs an email.

[0117] The email delivery unit can estimate the user's emotions and adjust the level of detail in the email based on the estimated emotions. The email delivery unit estimates the user's emotions using, for example, an emotion engine or generative AI. For example, the email delivery unit analyzes the user's facial expressions using facial recognition technology and estimates their emotions. By adjusting the level of detail in the email based on the user's emotions, the email is provided in a way that is easy for the user to understand. User emotions include, but are not limited to, joy, sadness, and anger. The level of detail in the email includes, but is not limited to, adding detailed explanations or including only the main points. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can adjust the level of detail using an AI model that takes user emotion data as input and outputs the level of detail in the email.

[0118] The email delivery unit can analyze participants' email open history when sending emails and determine the optimal delivery timing. For example, the email delivery unit can use AI to analyze participants' email open history and determine the optimal delivery timing when sending emails. For example, the email delivery unit can determine the optimal delivery timing based on participants' email open history. By analyzing participants' email open history and determining the optimal delivery timing, the email open rate can be improved. Email open history includes, but is not limited to, examples such as open time and open frequency. Optimal delivery timing includes, but is not limited to, examples such as selection of delivery time and delivery date. Some or all of the above processing in the email delivery unit may be performed using AI or not. For example, the email delivery unit can determine the timing using an AI model that takes participants' email open history as input and outputs the optimal delivery timing.

[0119] The email distribution unit can automatically attach additional information related to the meeting topic when sending emails. For example, the email distribution unit can use AI to automatically attach additional information related to the meeting topic when sending emails. This allows participants to quickly obtain the necessary information by automatically attaching additional information related to the meeting topic. This additional information may include, but is not limited to, external resources, literature, data, and statistics. Some or all of the above processing in the email distribution unit may be performed using AI or not. For example, the email distribution unit can attach information using an AI model that takes the meeting topic as input and outputs additional information.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The meeting preparation efficiency system can also include an agenda customization section that customizes agenda items based on the participants' areas of expertise. This section automatically suggests relevant agenda items based on the participants' fields of expertise. For example, if there are many participants from the technical department, technical topics can be prioritized. Similarly, if there are many participants from the marketing department, topics related to marketing strategy can be suggested. This provides an agenda tailored to the participants' areas of expertise, improving meeting efficiency. Furthermore, the agenda customization section can analyze past statements made by participants and suggest relevant topics. For example, revisiting themes frequently discussed in past meetings can facilitate continued discussion.

[0122] The meeting preparation efficiency system can also include an industry trend integration unit that automatically incorporates the latest industry trends related to the meeting topic. This unit automatically collects the latest news and reports related to the meeting topic and reflects them in the agenda. For example, in the case of a meeting about new product development, it can incorporate the latest market trends and competitor activities. Furthermore, the industry trend integration unit can collect expert opinions and analyses related to the meeting topic and reflect them in the agenda. This ensures that discussions are based on the latest information, improving the quality of the meeting.

[0123] The meeting preparation efficiency system may also include an agenda expression adjustment unit that estimates the user's emotions and adjusts the way the agenda is presented based on those emotions. The agenda expression adjustment unit adjusts the wording and visual emphasis of the agenda based on the user's emotions. For example, if the user is stressed, concise and easy-to-understand language can be used. Conversely, if the user is excited, detailed explanations and visual emphasis can be added. This provides an agenda that is easy for the user to understand, streamlining meeting preparation.

[0124] The meeting preparation efficiency system can also include a document collection unit that automatically gathers and presents relevant materials and data to the user when the meeting theme and objectives are entered. The document collection unit automatically collects and displays relevant past meeting materials and data based on the meeting theme and objectives. For example, in the case of a meeting about new product development, it can automatically collect and present past market research results and development schedules. The document collection unit can also search external resources and literature to gather relevant information. This streamlines meeting preparation and allows for quick access to necessary information.

[0125] The meeting preparation efficiency system can further include a speech timing adjustment unit that estimates the user's emotions and adjusts the timing of prompting participation based on those emotions. The speech timing adjustment unit adjusts the timing of prompting participation based on the user's emotions. For example, if a user is nervous, it can give them time to relax before prompting them to speak. Conversely, if a user is excited, prompting them to speak immediately can stimulate discussion. This ensures that participation is prompted at the appropriate time, leading to a smoother meeting.

[0126] The meeting preparation efficiency system can further include a speech algorithm application unit that applies different speech facilitation algorithms depending on the progress of the meeting. The speech algorithm application unit applies the most suitable speech facilitation algorithm according to the progress of the meeting. For example, if the meeting is behind schedule, an algorithm that quickly advances the discussion can be applied. Conversely, if the meeting is progressing smoothly, an algorithm that encourages detailed discussion can be applied. This ensures that optimal speech facilitation is performed according to the progress of the meeting, and the discussion proceeds effectively.

[0127] The meeting preparation efficiency system can also include a meeting minutes expression adjustment unit that estimates the user's emotions and adjusts the way the meeting minutes are presented based on those emotions. The meeting minutes expression adjustment unit adjusts the wording and visual emphasis of the minutes based on the user's emotions. For example, if the user is stressed, concise and easy-to-understand language can be used. Conversely, if the user is excited, detailed explanations and visual emphasis can be added. This provides meeting minutes that are easy for users to understand and streamlines post-meeting follow-up.

[0128] The meeting preparation efficiency system can also include a meeting minutes format suggestion unit that automatically proposes the optimal format when creating meeting minutes by referring to past meeting minutes. The meeting minutes format suggestion unit automatically proposes relevant formats based on past meeting minutes. For example, it can suggest relevant formats based on formats used in past meetings. Furthermore, the meeting minutes format suggestion unit can also propose the optimal format according to the meeting's theme and purpose. This streamlines meeting minute creation and ensures that important information is recorded without omission.

[0129] The meeting preparation efficiency system can also include an email expression adjustment unit that estimates the user's emotions and adjusts the email's wording based on those emotions. The email expression adjustment unit adjusts the wording and visual emphasis of the email based on the user's emotions. For example, if the user is stressed, concise and easy-to-understand language can be used. If the user is excited, detailed explanations and visual emphasis can be added. This provides emails that are easy for users to understand, and information sharing becomes more efficient.

[0130] The meeting preparation efficiency system can also include an email timing suggestion unit that proposes the optimal delivery time by referring to past email delivery history. The email timing suggestion unit proposes the optimal delivery time based on past email delivery history. For example, it can analyze past email delivery history and suggest the time slot with the highest open rate. Furthermore, the email timing suggestion unit can determine the optimal delivery time based on participants' email open history. This improves email open rates and streamlines information sharing.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk inputs the meeting theme and purpose. The reception desk provides an interface for users to input the meeting theme and purpose, allowing them to input the theme and purpose using text or voice input. For example, speech recognition technology can be used to convert the user's voice into text and input it as the meeting theme and purpose. Step 2: The agenda creation department automatically generates a draft agenda based on the information entered by the reception department. The agenda creation department uses a generation AI to generate a draft agenda based on the meeting's theme and objectives. For example, if the meeting's theme is "New Product Development," it automatically creates agenda items such as "Market Research Results Report," "Development Schedule Confirmation," and "Budget Review." Step 3: The schedule confirmation unit checks participants' schedules and presents possible dates. Based on participants' calendar information, the schedule confirmation unit proposes the most suitable meeting date. For example, by automatically generating candidate dates that take everyone's availability into consideration, scheduling can be made smoothly. Step 4: The discussion facilitation unit encourages participation during the meeting and provides summaries and closing comments. The discussion facilitation unit uses generative AI to provide comments that encourage participation and support the progress of the discussion in real time. For example, if the discussion stalls, it will provide comments such as, "Let's move on to the next agenda item." It also presents summaries of the content and provides closing comments in accordance with the progress of the meeting. Step 5: The minutes creation department automatically generates meeting minutes after the meeting. The minutes creation department uses a generation AI to automatically summarize the content of the discussions during the meeting and create the minutes. For example, it extracts the important points and decisions made during the meeting and compiles them into the minutes. Step 6: The email distribution department distributes the meeting minutes via email. The email distribution department uses generation AI to automatically create emails based on the generated meeting minutes and distributes them to participants. For example, it automatically compiles the meeting minutes, including important points and decisions, into an email and distributes it to participants.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, agenda creation unit, schedule confirmation unit, participation facilitation unit, meeting minutes creation unit, and email distribution unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input the theme and purpose of the meeting. The agenda creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a draft agenda using generation AI. The schedule confirmation unit is implemented by, for example, the control unit 46A of the smart device 14 and proposes the optimal meeting schedule based on the participants' calendar information. The participation facilitation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides comments that encourage participation in real time and comments that support the progress of the discussion. The meeting minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically compiles the content of the discussion during the meeting and creates meeting minutes. The email distribution unit is implemented, for example, by the control unit 46A of the smart device 14, which automatically creates an email based on the generated meeting minutes and distributes it to the participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, agenda creation unit, schedule confirmation unit, participation facilitation unit, meeting minutes creation unit, and email distribution unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input the theme and purpose of the meeting. The agenda creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a draft agenda using generation AI. The schedule confirmation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and proposes the optimal meeting schedule based on the participants' calendar information. The participation facilitation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides comments that encourage participation in real time and comments that support the progress of the discussion. The meeting minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically compiles the content of the discussion during the meeting and creates meeting minutes. The email distribution unit is implemented, for example, by the control unit 46A of the smart glasses 214, which automatically creates emails based on the generated meeting minutes and distributes them to participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the reception unit, agenda creation unit, schedule confirmation unit, participation facilitation unit, meeting minutes creation unit, and email distribution unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input the theme and purpose of the meeting. The agenda creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a draft agenda using generation AI. The schedule confirmation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes the optimal meeting schedule based on the participants' calendar information. The participation facilitation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides comments that encourage participation in real time and comments that support the progress of the discussion. The meeting minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically compiles the content of the discussion during the meeting and creates meeting minutes. The email distribution unit is implemented, for example, by the control unit 46A of the headset terminal 314, which automatically creates an email based on the generated meeting minutes and distributes it to the participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the reception unit, agenda creation unit, schedule confirmation unit, participation facilitation unit, meeting minutes creation unit, and email distribution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input the theme and purpose of the meeting. The agenda creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates a draft agenda using generation AI. The schedule confirmation unit is implemented by, for example, the control unit 46A of the robot 414 and proposes the optimal meeting schedule based on the participants' calendar information. The participation facilitation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides comments that encourage participation in real time and comments that support the progress of the discussion. The meeting minutes creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically compiles the content of the discussion during the meeting and creates meeting minutes. The email distribution unit is implemented, for example, by the control unit 46A of robot 414, which automatically creates emails based on the generated meeting minutes and distributes them to participants. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A reception desk where you enter the meeting theme and purpose, An agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit, The schedule confirmation unit checks the participants' schedules and presents possible dates, The speech facilitation department encourages participation during meetings and provides summaries and closing comments. The minutes creation department automatically generates meeting minutes after the meeting, It includes an email distribution department that distributes meeting minutes via email. A system characterized by the following features. (Note 2) The agenda creation unit, Generate a draft agenda based on the meeting's theme and objectives. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned schedule confirmation unit, We will suggest the most suitable meeting date based on the participants' calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned speech promotion unit, Provides comments that encourage participation in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned speech promotion unit, Present summaries of the meeting content as it progresses and provide closing remarks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned minutes preparation department, Automatically summarizes what was said during the meeting and creates meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned email distribution department, The generated meeting minutes are used to automatically create and distribute emails to participants. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how the meeting topic and objectives are entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Referencing past meeting themes and objectives, the system automatically suggests similar topics. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter the meeting theme and objectives, relevant materials and data are automatically collected and presented to them. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered themes and objectives based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter the meeting topic and objectives, the system analyzes their past conversation history and automatically completes relevant keywords. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering the meeting theme and purpose, the system provides the most suitable input format based on the user's job responsibilities and position. The system described in Appendix 1, characterized by the features described herein. (Note 14) The agenda creation unit, It estimates the user's emotions and adjusts how the agenda is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The agenda creation unit, When creating an agenda, the system automatically suggests the most suitable items by referring to past meeting agendas. The system described in Appendix 1, characterized by the features described herein. (Note 16) The agenda creation unit, When creating the agenda, apply different agenda generation algorithms depending on the purpose of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 17) The agenda creation unit, It estimates the user's sentiment and adjusts the level of detail in the agenda based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The agenda creation unit, When creating the agenda, customize the agenda items based on the expertise of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 19) The agenda creation unit, When creating the agenda, the system automatically incorporates the latest industry trends related to the meeting topic. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned schedule confirmation unit, The system estimates the user's emotions and prioritizes potential dates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned schedule confirmation unit, When confirming the schedule, we will suggest the most suitable meeting location, taking into account the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned schedule confirmation unit, When checking the schedule, participants' calendar information is updated in real time to reflect the latest schedule. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned speech promotion unit, It estimates the user's emotions and adjusts comments to encourage participation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned speech promotion unit, When encouraging participation, refer to comments made in past meetings to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned speech promotion unit, When facilitating participation, different participation facilitation algorithms are applied depending on the progress of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned speech promotion unit, It estimates the user's emotions and adjusts the timing of prompting them to speak based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned speech promotion unit, When facilitating discussion, provide comments that are most appropriate based on the participants' areas of expertise and roles. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned speech promotion unit, During discussions, provide real-time updates on the latest information related to the meeting topic. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned minutes preparation department, The system estimates the user's emotions and adjusts the way the meeting minutes are written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned minutes preparation department, When creating meeting minutes, the system automatically suggests the optimal format by referring to past meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned minutes preparation department, When creating meeting minutes, different minute-generating algorithms are applied depending on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned minutes preparation department, The system estimates the user's emotions and adjusts the level of detail in the meeting minutes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned minutes preparation department, When creating meeting minutes, customize the content of the minutes based on the frequency of participation in the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned minutes preparation department, When creating meeting minutes, reference materials related to the meeting topic will be automatically attached. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned email distribution department, It estimates the user's emotions and adjusts the email's wording based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned email distribution department, When sending emails, we refer to past email delivery history to suggest the optimal delivery timing. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned email distribution department, When sending emails, different email delivery algorithms are applied depending on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned email distribution department, The system estimates the user's sentiment and adjusts the level of detail in emails based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned email distribution department, When sending emails, we analyze participants' email open history to determine the optimal delivery timing. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned email distribution department, When sending emails, automatically attach additional information related to the meeting topic. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where you enter the meeting theme and purpose, An agenda creation unit automatically creates a draft agenda based on the information entered by the reception unit, The schedule confirmation unit checks the participants' schedules and presents possible dates, The speech facilitation department encourages participation during meetings and provides summaries and closing comments. The minutes creation department automatically generates meeting minutes after the meeting, It includes an email distribution department that distributes meeting minutes via email. A system characterized by the following features.

2. The agenda creation unit, Generate a draft agenda based on the meeting's theme and objectives. The system according to feature 1.

3. The aforementioned schedule confirmation unit, We will suggest the most suitable meeting date based on the participants' calendar information. The system according to feature 1.

4. The aforementioned speech promotion unit, Provides comments that encourage participation in real time. The system according to feature 1.

5. The aforementioned speech promotion unit, Present summaries of the meeting content as it progresses and provide closing remarks. The system according to feature 1.

6. The aforementioned minutes preparation department, Automatically summarizes what was said during the meeting and creates meeting minutes. The system according to feature 1.

7. The aforementioned email distribution department, The generated meeting minutes are used to automatically create and distribute emails to participants. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how the meeting topic and objectives are entered based on the estimated emotions. The system according to feature 1.

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